涉及或隐含距离计算的算法,比如K-means、KNN、PCA、SVM等,一般需要feature scaling,因为: zero-mean一般可以增加样本间余弦距离或者内积结果的差异,区分力更强,假设数据集集中分布在第一象限遥远的右上角,将其平移到原点处,可以想象样本间余弦距离的差异被放大了。在模版匹配中,zero-mean可以明显提高响应结果的区分度。
数据归一化(Feature Scaling) 之前我们使用 kNN 算法来完成分类这个任务的时候,其实少做了非常重要的一步就是数据归一化(Feature Scaling)。 首先,我们来看看,为什么我们需要做数据归一化?我依然举出肿瘤这个例子,假设我们有两个特征,一个是肿瘤大小(单位是cm),一个是发现天数(单位是天)。样本1中肿...
Make sure features are on a similar scale 数据归一化后,最优解的寻优过程明显会变得平缓,更容易正确的收敛到最优解。什么时候需要featurescaling? 涉及或隐含距离计算的算法,比如K-means、KNN、PCA、SVM等,一般需要featurescaling, 损失函数中含有正则项时,一般需要featurescaling梯度下降算法 ...
Please note that not all ML algorithms will require feature scaling. The general rule of thumb is that algorithms that exploit distances or similarities between data samples, such as artificial neural network (ANN), KNN, support vector machine (SVM), and k-means clustering, are sensitive to ...
For instance, algorithms like logistic regression, support vector machines (SVM), multilayer perceptrons (MLP), and k-nearest neighbors (kNN) exhibit better performance when feature scaling is implemented. In contrast, tree-based models like decision trees, random forests, and gradient boosting are ...
linear-regressionlogistic-regressionknndecision-tree-classifierclassification-algorithmdbscan-clusteringarima-modeldecision-tree-regressionagglomerative-clusteringdbscan-clustering-algorithmelasticnetregressionrandomized-search-cross-validationfeatureselectionelasticnet-regressionploynomialregressionhandling-imbalanced-data ...
Scaling For example: if we apply KNN algorithm to the instances below, as we see in the second row, we caculate the distance between the instance and the object. It is obvious that dimension of large scale dominates the distance. Tree-based models doesn’t depend on scaling ...
Key Activities in Feature Engineering: Handling Missing Values: Replacing or imputing missing values with mean, median, mode, or more complex methods like KNN imputation. Scaling and Normalizing: Adjusting features to a common scale to avoid issues caused by varying magnitudes. Encoding Categorical Dat...
这种转换保留了数据点之间的距离信息,这对于估计距离的算法(kNN,SVM,k-means等)是很重要的。 fromscipy.spatialimportdistance euclidean(make_harmonic_features(23), make_harmonic_features(1))# output 0.5176380902050424euclidean(make_harmonic_features(9), make_harmonic_features(11))# output 0.5176380902050414eu...
In our experiment, the performance of the proposed algorithm is better than RADAR, FS-kNN and CFS-kNN, in term of accuracy. Moreover, ANN is more flexible and reasonable than other fixed structure schemes to determine the weights used in the feature-scaling based algorithms. The algorithm is...